论文标题
人本体人群的神经建筑
A Neural Architecture for Person Ontology population
论文作者
论文摘要
一个人的本体论包括概念，属性和人际关系在数据保护，授课，知识图中的商业智能和预防知识图中有许多应用。尽管人工神经网络导致了实体识别，实体分类和关系提取的改善，但创建本体学很大程度上仍然是手动过程，因为它需要概念之间的固定语义关系。在这项工作中，我们提出了一个系统，用于使用实体分类和关系提取的神经模型从非结构化数据自动填充人本体图。我们为这些任务介绍了一个新数据集并讨论我们的结果。
A person ontology comprising concepts, attributes and relationships of people has a number of applications in data protection, didentification, population of knowledge graphs for business intelligence and fraud prevention. While artificial neural networks have led to improvements in Entity Recognition, Entity Classification, and Relation Extraction, creating an ontology largely remains a manual process, because it requires a fixed set of semantic relations between concepts. In this work, we present a system for automatically populating a person ontology graph from unstructured data using neural models for Entity Classification and Relation Extraction. We introduce a new dataset for these tasks and discuss our results.
